Everything in Monitoring & SRE, page 3
Why tail latency (p99) matters
Averages hide the worst experiences, and fan-out amplifies tails so most requests touch a slow path; causes include GC pauses, queueing, contention, and noisy neighbors.
Front-end performance budgets
A performance budget is an enforced limit on metrics like Core Web Vitals and bundle size, checked in CI to fail builds that regress.
Little's Law for capacity planning
L equals lambda times W, concurrency equals arrival rate times time in system; rearrange to size threads or concurrency for a target throughput and latency.
Capacity planning for annual traffic spikes
Forecast peak from history, load-test to find per-unit capacity, use autoscaling with pre-warming and headroom, and protect with caching and graceful degradation.
Diagnose database CPU saturation under load
Find the expensive queries via the database's stats, check for missing indexes and full scans, then fix with indexing, query rewrites, caching, or read replicas.
Load vs stress vs soak testing
Load tests expected traffic, stress pushes past limits to find the breaking point, soak runs sustained load for hours to expose leaks.
Golden signals for capacity planning
Monitor the four golden signals, latency, traffic, errors, and saturation, from day one, watching percentiles and saturation to forecast scaling.
Canary a shared downstream microservice
Route a slice of traffic to the canary via mesh rules, propagate context, and use distributed tracing to measure impact on upstream callers across the full path.
Auto-rollback on failed blue-green cutover
Shift traffic gradually behind a smart router, use deep health checks plus real SLI monitoring, and auto-revert to blue on breach while blue stays warm.
Client-side vs server-side feature flags
Client-side is fast and offline-capable but exposes flag logic and risks stale or leaked values; server-side keeps logic secret and consistent but adds latency.
Design automated canary analysis scoring
Track the golden-signal SLIs, compare canary to baseline statistically, weight and combine into a score with promote/rollback thresholds.
Diagnose a degraded canary release
Check statistical significance versus baseline, confirm apples-to-apples comparison, isolate the cause via traces and logs, then weigh the regression against SLO budget.
Blue-green deploys with schema migrations
The shared database means both versions hit one schema, so breaking changes must be split into backward-compatible steps via expand-and-contract.
Measure ROI of toil reduction efforts
Track toil hours, percent of time on toil, incidents auto-resolved, and engineer cost saved, then frame as ROI and risk reduction.
Design a centralized auto-remediation platform
Event ingestion, a rules engine mapping alerts to playbooks, a sandboxed execution runtime, and guardrails like dry-run, rate limits, and rollback.
Design automated microservice provisioning workflow
Template scaffolding plus a pipeline that creates repo, CI/CD, and infra as code, with idempotent steps and rollback.
Systematically reduce noisy alert toil
Inventory alerts, measure frequency, actionability, and time cost, then prioritize by volume times effort.
Idempotency in infrastructure provisioning scripts
Idempotency means repeated runs converge to one end state; achieve it via desired-state reconciliation or idempotency keys with read-before-write.
Automate temporary elevated database access securely
Self-service request with approval, short-lived auto-expiring grants scoped to least privilege, and full audit logging.
Distinguishing toil from necessary ops work
Toil is manual, repetitive, automatable, tactical, and scales with growth; engineering and judgment-heavy work is not toil.
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